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| United States Patent |
6,144,923
|
|
Grosse
|
November 7, 2000
|
Machine diagnosis system
Abstract
The invention, that relates to a machine diagnosis system for the
state-oriented operation monitoring of a machine, comprises a
characteristic value module and a cause module. The characteristic value
module, proceeding from machine-state referred measurement values,
establishes diagnosis-relevant characteristic values, and the cause module
diagnoses from the characteristic values a cause for the detected
measurement values. There the cause module calculates for at least one
possible cause in each case a cause probability which indicates with what
probability the corresponding cause is responsible for the presence of the
detected oscillation measurement values.
| Inventors:
|
Grosse; Gilbert (Heidenheim, DE)
|
| Assignee:
|
Voith Hydro, GmbH & Co KG (DE)
|
| Appl. No.:
|
019619 |
| Filed:
|
February 6, 1998 |
Foreign Application Priority Data
| Feb 22, 1997[DE] | 197 07 173 |
| Current U.S. Class: |
702/56; 700/159; 700/169; 702/179; 702/185 |
| Intern'l Class: |
G01F 017/00 |
| Field of Search: |
714/25
700/159,169
702/179,185,56
73/659
|
References Cited
U.S. Patent Documents
| 4425798 | Jan., 1984 | Nagai et al. | 73/659.
|
| 4812976 | Mar., 1989 | Lundy | 364/413.
|
| 4985857 | Jan., 1991 | Bajpai et al.
| |
| 4989159 | Jan., 1991 | Liszka et al. | 364/508.
|
| 5210704 | May., 1993 | Husseiny | 364/551.
|
| 5251151 | Oct., 1993 | Demjanenko et al. | 364/550.
|
| 5566092 | Oct., 1996 | Wang et al. | 364/551.
|
| 5594175 | Jan., 1997 | Lyon et al. | 73/593.
|
| 5602761 | Feb., 1997 | Spoerre et al. | 364/554.
|
| 5661668 | Aug., 1997 | Yemini et al. | 364/550.
|
| 5710715 | Jan., 1998 | Shitanda | 364/508.
|
| 5943634 | Aug., 1999 | Piety et al. | 702/56.
|
Other References
Kay, Steven M. and Marple, Stanley Lawrence, Spectrum Analysis--A Modern
Perspective, Proceedings of the IEEE, Nov., 1981, 1380-1419.
Voith Group of Companies--Voith Hydro Turbine Group--Dimensions and
Illustrations, Jul. 1995 (1 Page, double sided--English language on one
side, German language on the other side).
|
Primary Examiner: Hua; Ly V.
Assistant Examiner: Crockett; Robert G.
Attorney, Agent or Firm: Foley & Lardner
Claims
What is claimed is:
1. Machine diagnosis system for state-oriented operation monitoring of a
machine, comprising:
a characteristic value module which, proceeding from machine state-related
measurement values, forms diagnosis-relevant characteristic values that
are linked to operating parameters; and
a cause module which, from the characteristic values, diagnoses a cause for
the detected measurement values,
wherein the cause module, for at least one possible cause in each case,
calculates a cause probability with the aid of at least one
probability-characteristic value relation described by a polygonal course,
which indicates with what probability the corresponding cause is
responsible for the presence of the detected measurement values.
2. Machine diagnosis system according to claim 1, wherein the cause module
for the calculation of the cause probability, calls upon, besides
individual characteristic values, at least one characteristic value
linkage described by a function of a t least two of the characteristic
values that have cross-linked causal interconnections between them.
3. Machine diagnosis system according to claim 1, wherein the cause module
calculates the cause probability as a weighted arithmetic mean of
individual probabilities, in which the individual probabilities are
allocated to individual characteristic values or to characteristic value
linkages and, for the mean value formation, are weighted with individual
weighting factors.
4. Machine diagnosis system according to claim 3, wherein the individual
weighting factors are influenced by characteristic values.
5. Machine diagnosis system according to claim 1, wherein the
characteristic value module forms characteristic values dependent on
operating parameters and that the cause module draws upon the dependence
of the characteristic values or of characteristic value linkages of the
operating parameters for the calculation of the cause probability.
6. Machine diagnosis system according to claim 5, wherein the
characteristic values or characteristic value linkages are time-dependent,
and wherein the characteristic value module assigns the characteristic
values or characteristic value linkages to certain operating parameter
intervals in order to form characteristic value-operating parameter
histograms.
7. Machine diagnosis system according to claim 1, wherein the machine is a
rotating hydraulic flow machine, the oscillations of which are to be
diagnosed.
8. Machine diagnosis system according to claim 1, wherein the at least one
polygonal course is established by a plurality of number pairs stored in a
knowledge basis.
9. Machine diagnosis system according to claim 7, wherein the spectral
constituents of at least one of shaft oscillations, orbit-characteristics
of the shaft oscillations, and phase angles of the shaft oscillations are
characteristic values.
10. Machine diagnosis system according to claim 5, wherein an operating
parameter is processed, which clearly indicates the momentary operation
type, namely whether one of a pump operation, a turbine operation, a phase
shifter operation, a starting of the machine, and a running-down of the
machine is present.
11. Machine diagnosis system according to claim 5, wherein at least one of
momentary performance of the machine, suction tube pressure, tube track
pressure, guide wheel position, and drop height of the hydraulic flow
machine, is an operating parameter.
12. Machine diagnosis system according to claim 1, wherein a warning module
warns the machine operator on presence of a certain cause probability or
of a certain distribution of cause probabilities.
13. Machine diagnosis system according to claim 1, wherein a reaction
module on presence of a certain cause probability or of a certain
distribution of cause probabilities can interfere into the operation of
the machine.
14. Machine diagnosis system according to claim 1, wherein the calculation
of the cause probability by the cause module proceeds from a
knowledge-basis which comprises machine-individual experience knowledge
and a theoretical machine model.
15. Machine diagnosis system according to claim 1, wherein the
characteristic value module for the formation of the characteristic values
proceeds from a knowledge-basis, which comprises machine-individual
experience knowledge and a theoretical machine model.
16. Machine diagnosis system according to claim 1, wherein the
knowledge-basis is extensible or modifiable by the experience of the
machine operator.
17. Process for diagnosing the state-oriented operation monitoring of a
machine, comprising:
forming diagnosis-relevant characteristic values that are linked to
operating parameters from machine state-referenced measurement values; and
diagnosing a cause for the detected measurement values from the
characteristic values;
further comprising for at least one possible cause in each case,
calculating a cause probability with the aid of at least one
probability-characteristic value relation described by a polygonal course,
which indicates with what probability the corresponding cause is
responsible for the presence of the detected measurement values.
18. Process according to claim 17, wherein for the calculation of the cause
probability, besides individual characteristic values, at least one
characteristic value linkage is drawn upon, the characteristic value
linkage being described by a function of at least two of the
characteristic values that have cross-linked causal interconnections
between them.
19. Process according to claim 17, wherein the cause probability is
calculated as weighted arithmetic mean of individual probabilities, in
which the individual probabilities allocated to individual characteristic
values or characteristic value linkages are weighted for mean value
formation with individual knowledge-based weighting factors.
20. Process according to claim 19, wherein the mean value formation occurs
with characteristic value-influenced weighting factors.
21. Process according to claim 17, further comprising:
forming characteristic values dependent on operating parameters, in which
operating parameters are operation type-referred measurement values, and
drawing upon the dependence of the characteristic values or characteristic
value linkages by the operating parameters for the calculation of the
cause probability.
22. Process according to claim 21, wherein the characteristic values or
characteristic value linkages are time-dependent, and wherein the
characteristic values or characteristic value linkages are allocated to
certain operating parameter intervals in order to form characteristic
value-operating parameter histograms.
23. Process according to claim 17, wherein the machine is a rotating
hydraulic flow machine, the oscillations of which are to be diagnosed.
24. Process according to claim 1, wherein the at least one polygonal course
is established by a plurality of number pairs stored in a knowledge basis.
25. Process according to claim 23, wherein at least one of the spectral
constituents of shaft oscillations, orbit characteristics of the shaft
oscillations, and phase angles of the shaft oscillations are
characteristic values.
26. Process according to claim 21, wherein an operating parameter is used
which indicates whether one of a pump operation, a turbine operation, a
phase shifter operation, a starting of the machine, and a running-down of
the machine is present.
27. Process according to claim 21, wherein at least one of momentary
performance of the machine, suction tube pressure, pipe track pressure,
guide wheel position, and fall height of the hydraulic flow machine, is
used as operating parameter.
28. Process according to claim 17, wherein on presence of a certain cause
probability or of a certain distribution of cause probabilities, the
machine operator is warned.
29. Process according to claim 17, wherein on presence of a certain cause
probability or of a certain distribution of cause probabilities, it is
possible to intervene in the operation of the machine.
30. Process according to claim 17, wherein the calculation of the cause
probability proceeds from a knowledge-basis which comprises
machine-individual experience knowledge and a theoretical machine model.
31. Process according to claim 17, wherein the formation of the
characteristic values proceeds from a knowledge basis which comprises
machine-individual experience knowledge and a theoretical machine model.
32. Process according to claim 30, wherein the machine operator can extend
or modify the knowledge basis with his rules.
Description
FIELD OF THE INVENTION
The invention relates to a machine diagnosis system for the state-oriented
operation monitoring of a machine with a characteristic value module
which, proceeding from machine state-related measurement values forms
diagnosis-relevant characteristic values and with a cause module which
diagnoses from the characteristic values a cause for the detected
oscillation measurement values, and to a process for the execution of a
corresponding machine diagnosis.
BACKGROUND OF THE INVENTION
Such a machine diagnosis system carrying out a diagnosis of the oscillation
state of water power machines is known from the firm publication t 2981
7.95 of the firm of Voith. Thereby impending damages can be perceived
early and the manner of operation of the machine can be optimized.
Rudiments for a computer-supported machine diagnosis system were presented
by other manufacturers of monitoring arrangements. It was a matter there,
however, either of simple mathematical analysis of the signals or there
was presumed a definition of oscillation patterns not feasible in actual
practice.
The problem of the invention, therefore, is to make available a machine
diagnosis system with which the diagnosis expenditure is reduced and the
dependability of the diagnosis is improved.
SUMMARY OF THE INVENTION
This problem is solved according to the invention by the means that the
cause module for at least one possible cause calculates in each case a
cause probability which indicates with what probability the corresponding
cause is responsible for the presence of the detected measurement values.
Thereby the diagnosis can proceed from a surveyable number of possible
causes for the detected measurement values and the diagnosis expenditure
can thus be kept within practicable limits.
If the cause module for the calculation of the cause probability, besides
individual characteristic values, draws upon at least one characteristic
value linkage, the accuracy and dependability of the diagnosis can be
increased, since through the use of characteristic value linkages there
can also be taken into account the influence of cross-linked causal
interconnections between characteristic values among one another and
actual machine state.
There has already been computed a cause module which detects the cause
probability as a weighted arithmetic mean of individual probabilities, in
which the individual probabilities are allocated to individual
characteristic values or characteristic value linkages and are weighted
for mean value formation with individual weighting factors. In this manner
there can enter into the decision as to whether a certain cause is
present, not only the characteristic value most obviously associated with
this cause, but also the influence of further characteristic values or
characteristic value linkages.
In the event that the individual weighting factors are characteristic
value-influenced, the cause probability calculation is so flexible that
only with the presence of possibilities opening up the presence of
intermediate results can possibilities plausibility checking still be
perceived by the cause module.
The characteristic value module forms characteristic values dependent on
operating parameters, in which operating parameters are measurement values
referred to type of operation. The cause module makes use of the
dependence of the characteristic values or characteristic value linkages
on the operating parameters for the calculation of the cause probability.
Underlying this measure there lies the insight that the representation of
the measurement values as a function of time is only slightly informative,
since the multiplicity of parameters that influence the machine behavior
is mixed in this representation. There the characteristic values or
characteristic value linkages can be time-dependent, in which case the
characteristic value module allocates the characteristic values or
characteristic value linkages to certain operating parameter intervals and
mediates them in time, in order to form operating parameter histograms.
Thereby the interrelations between characteristic values and oscillation
causes can be especially differentiated and exactly detected, in which
process a special trend analysis can evaluate the dependence of the
operating parameters as also on time.
It has proved that the machine diagnosis system of the invention is suited
especially for rotating machines and especially for hydraulic flow
machines, for example turbines.
For such machines, for example, the spectral components of the shaft
oscillations, the orbit characteristics of the shaft oscillations and the
phase angles of the shaft oscillation are significant and
diagnosis-relevant characteristic values. There it is advantageous for the
diagnosis success to take into account the dependence of these or of
further characteristic values on an operating parameter which indicates
whether a pump operation, a turbine operation, a phase shifter operation,
a starting of the machine, or a running-out of the machine is present.
Furthermore, however, there have also proved useful the operating
parameters (such as) momentary performance of the machine, suction tube
pressure, pipe course pressure, guide wheel position and drop height for a
hydraulic flow machine.
A warning module integrated into the diagnosis system also gives to the
machine operator--in the presence of a certain cause probability or of a
certain distribution of cause probabilities--besides the warning, the
probability for the existing fault, so that this operator can take
suitable steps on his own responsibility or, in the case of unusual
circumstances known to him, can ignore the warning.
In addition, or alternatively, an example of execution of the machine
diagnosis system, however, can also comprise a reaction module which, in
the presence of a certain cause probability or of a certain distribution
of cause probabilities, can intervene in the operation of the machine.
Such an incursion can be for example a power limit, a load change, or a
machine stoppage. Hereby an automatic system is made available, largely
precluding a human failing as source of error.
The calculation of the cause probability by the cause module or the
formation of the characteristic values by the characteristic value module
proceeds from a knowledge-basis which comprises machine-individual
experience knowledge and a theoretical machine model. Thereby all
experiences or existing knowledge gained by the manufacturer and/or
machine operator are utilized. In view of a training phase accompanying
the operating of the machine in the machine diagnosis system, it is
advantageous there if the knowledge basis is extensible and/or modifiable
on the part of the machine operator.
Further, the invention comprises the process according to claims 17 to 32.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention is explained in the following with the aid of forms of
execution with reference to the attached figures, in which:
FIG. 1 shows a schematic representation of a form of execution of the
machine diagnosis system according to the invention;
FIG. 2 shows a representation explaining the function of the characteristic
value module of FIG. 1;
FIG. 3 shows a representation explaining the function of the cause module
of FIG. 1; and
FIG. 4 shows a schematic representation of a further form of execution of
the machine diagnosis system according to the invention.
DETAILED DESCRIPTION OF A PREFERRED EXEMPLARY EMBODIMENT
The schematic representation of the machine diagnosis system according to
the invention in FIG. 1 shows a characteristic value module 1 which
establishes diagnosis-relevant characteristic values 5 from measurement
values 3 detected on a machine. A cause module 7 calculates successively
from these characteristic values 5 a series of cause probabilities
W.sub.1, W.sub.2, . . . W.sub.k. The cause probability W.sub.1, W.sub.2 or
W.sub.k is the probability the cause 1, 2 or k that underlies the
measurement values 3.
FIG. 1 shows a form of execution in which the characteristic module 1
establishes the characteristic values 5 with the aid of a knowledge basis
9. The knowledge-basis 9 in the form of execution according to FIG. 1,
however, underlies also the calculation of the cause probabilities
W.sub.1, W.sub.2, . . . W.sub.k and it also comprises, besides
machine-individual experience knowledge of the machine manufacturer and
possibly of the machine operator, a theoretical machine model, whereby the
diagnosis relevance of the characteristic values can depend also on the
quality of the theoretical modeling of the machine.
The invention, however, is not restricted to the form of execution
schematically represented in FIG. 1. Thus the formation of the
characteristic values 5 or the calculation of the cause probabilities
W.sub.1, W.sub.2, . . . W.sub.k also can occur without resort to the
knowledge-basis 9. What is essential is the formation of
machine-dependent, easily handleable, diagnosis-relevant characteristic
values 5 from the primary measurement values 3. The characteristic values
5 can thereby be formed in such manner that they are symptomatic or
characteristic for one or several definite causes. Also thereby the
diagnosis expenditure can be kept within practicable limits with great
certainty and dependability.
With use of the machine diagnosis system of the invention for oscillation
diagnosis in rotating water power machines, the measurement values 3 can
comprise relative shaft oscillations measured at different points of the
machine shaft of the water power installation. Further measurement values
in this case can be the turning rate of the shaft, the effective or idle
power, the suction pipe pressure, the pipeline pressure or the guide wheel
position. The characteristic values 5 formed from such measurement values
3 may include, for example, the spectral constituents of the shaft
oscillations, such as, for example the turning rate harmonic, the turning
rate subharmonic or also the turning rate interharmonic spectral
constituents. Besides these, it has proved that also the orbit
characteristic of the shaft oscillations is a diagnosis-relevant
characteristic value. To this there belongs, for example, the main axle
relation and the orientation or the ellipse allocated to the first
turning-rate harmonic constituent of the shaft oscillations. Also the
relative alignment of such ellipses at different measuring sites of the
shaft are possible characteristic values. Further examples for
characteristic values also are the phase angle also for the higher
harmonic constituents of the oscillations, precession movements, or also
standard deviations, time mean values, or peak values of such magnitudes.
Selection and type for the determination of these characteristic values can
be established in the conceptual knowledge-basis 9. Also, the cause module
7 can be based on such expert knowledge stored in the knowledge-basis 9.
It has proved that characteristic values 5 formed from the direct or also
pre-analyzed measurement values 3 for certain oscillation causes can be
far more characteristic than these measurement values 3 themselves. This
experience knowledge, which can be supported and developed by the
theoretical modeling of the water power machine, can underlie the cause
probability calculation by the cause module 7, which is represented in
symbolic manner by the arrow 10 proceeding from the knowledge-basis 9 and
ending on the cause module 7. A possible cause of the detected measurement
values can be, for example, a coupling error to which according to
experience above all of the characteristic value of the first turning-rate
harmonic spectral constituent of the shaft oscillation is allocated. The
cause of alignment errors, in contrast, can be recognized most clearly on
the characteristic value turning-rate dependence of the main axes of the
oscillation orbits.
In FIG. 2 there is represented an especially advantageous characteristic
value formation. This characteristic value formation is explained on the
basis of an oscillation measurement magnitude S, for example an
oscillation amplitude. The oscillation measurement magnitude S is measured
time-dependently and is represented in FIG. 2 as an S-t-diagram.
Simultaneously with the detection of the measurement value S there are
measured magnitudes designated as operating parameter-performance P, flow
Q and drop height H. From the measurement value S the characteristic value
module 1 establishes the characteristic value KW.sub.1, for example the
first turning-rate harmonic spectral constituent of the oscillation
amplitude. Further diagnosis-relevant characteristic values are formed by
the characteristic module 1 by allocation of the characteristic value
KW.sub.1 to various intervals of the operating parameters P, Q and H.
Therewith there are obtained characteristic values which are representable
as characteristic value-operating parameter histograms 12a, 12b, 12c, or
as functions of the individual operating parameters Pj, for example
KW.sub.1 =f.sub.1 (Pj), j=1, 2, . . . Since by reason of the multiplicity
of the parameters which influence the oscillation behavior of the machine
the representation of measurement magnitudes--made available by a
measuring device and possibly by an analysis system--as a function of time
is only slightly informative, it becomes possible by the linkage of the
oscillation characteristic values with operating parameters, and possibly
by the averaging over different periods of time, for example days, weeks,
months etc. a reasonable allocation of the time course of characteristic
values to certain operating states. The characteristic values KW.sub.i,
i=1 . . . are deposited in a long-time storage 11.
In FIG. 3 there is schematically represented how the cause module 7 of FIG.
1 calculates the probability W.sub.k for the presence of the cause k from
these characteristic values KW.sub.1, KW.sub.2 . . . stored in the
long-time storage 11.
From characteristic values KW.sub.1, KW.sub.1 =f.sub.1 (Pj) j=1, . . . , as
function of the operating parameters Pj of the characteristic value module
1, the cause module 7 calculates the probability established constituent
uw.sub.1k allocated to this characteristic value amount KW.sub.1, with the
aid of a uw.sub.1k -KW.sub.1, relation described by a polygonal course.
There, the representation of the uw.sub.1k -KW.sub.1 relation chosen in
FIG. 3 as an x-y diagram, is to be understood only symbolically. It is
possible, namely, to assign to any of the characteristic values KW.sub.1
covered by the function f.sub.1 (Pj) to such an x-y diagram with possibly
individual polygonal course. The calculation of individual probability
constituents uw.sub.1k from the characteristic values KW.sub.1 is
represented by the solid connecting line 13 between KW.sub.1 and the
uw.sub.1k -KW.sub.1 diagram. In a similar manner the probability
constituent UW.sub.2k contributing to the cause k, which is due to the
characteristic value amount KW.sub.2 =f.sub.2 (Pj), Pj=1, . . . , is
represented by the broken connecting line 15.
A quite especially advantageous form of execution of this invention also
considers, for the calculation of the cause probability W.sub.k, linkages
of characteristic values. Such a linkage, for example, of the
characteristic values KW.sub.1 and KW.sub.2 is represented in FIG. 3 by
the linkage symbol 17, into which the solid connecting line 19 proceeding
from the characteristic value KW.sub.1, and the broken connecting line 21
proceeding from the characteristic value KW.sub.2 issue. The linkage
symbol 17 can signify any conceivable linkage, for example the amount of
the difference of the characteristic values KW.sub.1 and KW.sub.2 :
.vertline.KW.sub.1 -KW.sub.2 .vertline. or any linkage described by an
arbitrary function of two variables KW.sub.1 and KW.sub.2. Besides this,
according to the invention, linkages of more than two characteristic
values also are possible.
The numerical value formed by this linkage 17 is used as input magnitude in
a uw.sub.(1,2)k -f(KW.sub.1,KW.sup.2) diagram. There, uw.sub.(1,2)k is the
share in the probability for the cause k which is due to the
characteristic value linkage f(KW.sub.1, KW.sub.2).
The particular form of the polygonal courses represented in FIG. 3 is
stored in the knowledge-basis 9 of FIG. 1. It has proved that already by
polygonal courses established by merely five number pairs there is made
possible a dependable machine diagnosis.
By the point symbols characterized with the reference numbers 23, 25 and 27
in FIG. 3, it is to be expressed that the cause module 7 can use more than
only the two probability constituent characteristic values represented,
and more than only the one probability constituent characteristic value
linkage relation.
From the respective values of the probability constituents UW.sub.1k,
UW.sub.2k . . . , UW.sub.(1,2)k, . . . then by multiplication with a
corresponding weighting factor G.sub.1k, G.sub.2k, . . . G.sub.(1,2)K, and
averaging of these weighted probability constituents, there is calculated
the cause probability W.sub.k. By the broken lines 29, 31 and 33 there is
represented the weighting with the weighting factors G.sub.1k, G.sub.2k
and G.sub.(1,2)k. Just as for the probability constituents uw.sub.1k, the
weighting factors G.sub.1k can also be dependent on the operating
parameters.
The applicant, however, has found that it is advantageous to make the
magnitude of the individual weighting factors G.sub.1k, G.sub.2k,
G.sub.(1,2)k . . . dependent on the characteristic values themselves. If,
for example, as characteristic value there has been formed the scatter of
a certain oscillation magnitude, it can be advantageous to give a
characteristic value allocated to the mean value of this oscillation
magnitude a greater weighting factor with smaller scatter, and a smaller
weighting factor with greater scatter. With the aid of the characteristic
value dependence of these individual weighting factors, plausibility
relations can thus be taken into account.
The formation of the arithmetic mean from the weighted probability
constituents is represented in FIG. 3 by the triangle designated with 35.
In FIG. 4 there is represented a further form of execution of the machine
diagnosis system according to the invention, in which the elements
corresponding to the form of execution of FIG. 1 have the same reference
numbers as in FIG. 1, increased by the number 100. For the explanation of
these elements, reference is made to the description for FIG. 1.
The probability W.sub.k calculated by the cause module 107 for the presence
of the cause k is transferred from the cause module 107 to a warning
module 137, which notifies or warns the operating personnel of a machine
139 to be diagnosed in the presence of a certain cause probability W.sub.k
or also of a certain distribution of cause probabilities. In a
corresponding manner the cause probabilities W.sub.k can also be
transferred to a reaction module 141, which in the given case can
automatically intervene in the operation of the machine 139.
The cause probabilities can further be presented to an expert editor 143
who not only informs the diagnosis expert about the state of the machine
139 but also gives him the possibility represented by the arrow 145 to
complete or to modify the machine diagnosis system. For this, for example,
a connection to the knowledge basis 109, represented by the connecting
line 147, is advantageous. In this manner there can enter into the
knowledge-basis 109 not only the experience and the machine understanding
of the machine manufacturer, but also the experience of the machine
operator gained in the course of the operation. The expert editor 143 also
permits, besides, the representation of intermediate results used for the
determination of the individual characteristic values, in order to make
possible for the diagnosis expert a monitoring of the machine diagnosis
system itself.
The form of execution of FIG. 4 has, further, a measurement value detection
module 149 which transfers measurement values 103 measured on the machine
139, for example a hydraulic flow machine, to the characteristic value
module 101.
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